Collaborative sensor data processing by deep learning accelerators with integrated random access memory

ABSTRACT

Systems, devices, and methods related to a Deep Learning Accelerator and memory are described. For example, an integrated circuit device may be configured to execute instructions with matrix operands and configured with random access memory. The random access memory is configured to store input data from a sensor, parameters of a first portion of an Artificial Neural Network (ANN), instructions executable by the Deep Learning Accelerator to perform matrix computation of the first portion of the ANN, and data generated outside of the device according to a second portion of the ANN. The Deep Learning Accelerator may execute the instructions to generate, independent of the data from the second portion of the ANN, a first output based on the input data from the sensor and generate a second output based on a combination of the data from the sensor and the data from the second portion of the ANN.

TECHNICAL FIELD

At least some embodiments disclosed herein relate to sensor fusion ingeneral and more particularly, but not limited to, sensor fusionimplemented via accelerators for Artificial Neural Networks (ANNs), suchas ANNs configured through machine learning and/or deep learning.

BACKGROUND

An Artificial Neural Network (ANN) uses a network of neurons to processinputs to the network and to generate outputs from the network.

For example, each neuron in the network receives a set of inputs. Someof the inputs to a neuron may be the outputs of certain neurons in thenetwork; and some of the inputs to a neuron may be the inputs providedto the neural network. The input/output relations among the neurons inthe network represent the neuron connectivity in the network.

For example, each neuron can have a bias, an activation function, and aset of synaptic weights for its inputs respectively. The activationfunction may be in the form of a step function, a linear function, alog-sigmoid function, etc. Different neurons in the network may havedifferent activation functions.

For example, each neuron can generate a weighted sum of its inputs andits bias and then produce an output that is the function of the weightedsum, computed using the activation function of the neuron.

The relations between the input(s) and the output(s) of an ANN ingeneral are defined by an ANN model that includes the data representingthe connectivity of the neurons in the network, as well as the bias,activation function, and synaptic weights of each neuron. Based on agiven ANN model, a computing device can be configured to compute theoutput(s) of the network from a given set of inputs to the network.

For example, the inputs to an ANN network may be generated based oncamera inputs; and the outputs from the ANN network may be theidentification of an item, such as an event or an object.

In general, an ANN may be trained using a supervised method where theparameters in the ANN are adjusted to minimize or reduce the errorbetween known outputs associated with or resulted from respective inputsand computed outputs generated via applying the inputs to the ANN.Examples of supervised learning/training methods include reinforcementlearning and learning with error correction.

Alternatively, or in combination, an ANN may be trained using anunsupervised method where the exact outputs resulted from a given set ofinputs is not known before the completion of the training. The ANN canbe trained to classify an item into a plurality of categories, or datapoints into clusters.

Multiple training algorithms can be employed for a sophisticated machinelearning/training paradigm.

Deep learning uses multiple layers of machine learning to progressivelyextract features from input data. For example, lower layers can beconfigured to identify edges in an image; and higher layers can beconfigured to identify, based on the edges detected using the lowerlayers, items captured in the image, such as faces, objects, events,etc. Deep learning can be implemented via Artificial Neural Networks(ANNs), such as deep neural networks, deep belief networks, recurrentneural networks, and/or convolutional neural networks.

Deep learning has been applied to many application fields, such ascomputer vision, speech/audio recognition, natural language processing,machine translation, bioinformatics, drug design, medical imageprocessing, games, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which like referencesindicate similar elements.

FIG. 1 shows an integrated circuit device having a Deep LearningAccelerator and random access memory configured according to oneembodiment.

FIG. 2 shows a processing unit configured to perform matrix-matrixoperations according to one embodiment.

FIG. 3 shows a processing unit configured to perform matrix-vectoroperations according to one embodiment.

FIG. 4 shows a processing unit configured to perform vector-vectoroperations according to one embodiment.

FIG. 5 shows a Deep Learning Accelerator and random access memoryconfigured to autonomously apply inputs to a trained Artificial NeuralNetwork according to one embodiment.

FIGS. 6-8 illustrate sensor fusion implemented in a Deep LearningAccelerator and random access memory configured according to someembodiments.

FIGS. 9-11 illustrate collaborative sensor data processing by DeepLearning Accelerators with random access memory configured according tosome embodiments.

FIG. 12 shows a method of sensor fusion according to one embodiment.

DETAILED DESCRIPTION

At least some embodiments disclosed herein provide an integrated circuitconfigured to perform sensor fusion using Artificial Neural Networks(ANNs) with reduced energy consumption and computation time. Theintegrated circuit includes a Deep Learning Accelerator (DLA) and randomaccess memory. The random access memory is configured to storeinstructions and parameters for the Deep Learning Accelerator (DLA) toimplement the computation of an Artificial Neural Network (ANN). Aplurality of such integrated circuit devices can be used to process thesensor data from a plurality of sensors respectively based on separateArtificial Neural Networks (ANNs). Further, the integrated circuits cancooperate with each other to generate an output of an Artificial NeuralNetwork (ANN) based on the combined sensor data from the plurality ofsensors. Instructions for the Deep Learning Accelerators in theintegrated circuit devices can be generated by a compiler optimizing thecomputation of the sensor fusion by reducing or eliminating overlappingprocessing between the separate Artificial Neural Networks (ANNs) forthe separate processing of the data from the sensors individually, andthe sensor fusion Artificial Neural Network (ANN) to generate an outputbased on the combined data from the sensors. For example, intermediateresults from the separate Artificial Neural Networks (ANNs) can beprovided as inputs to the sensor fusion Artificial Neural Network (ANN);and the inputs and further computation of sensor fusion can bedynamically distributed to one or more of the integrated circuit devicesbased on the workloads of the integrated circuit devices.

The Deep Learning Accelerator (DLA) includes a set of programmablehardware computing logic that is specialized and/or optimized to performparallel vector and/or matrix calculations, including but not limited tomultiplication and accumulation of vectors and/or matrices.

Further, the Deep Learning Accelerator (DLA) can include one or moreArithmetic-Logic Units (ALUs) to perform arithmetic and bitwiseoperations on integer binary numbers.

The Deep Learning Accelerator (DLA) is programmable via a set ofinstructions to perform the computations of an Artificial Neural Network(ANN).

The granularity of the Deep Learning Accelerator (DLA) operating onvectors and matrices corresponds to the largest unit of vectors/matricesthat can be operated upon during the execution of one instruction by theDeep Learning Accelerator (DLA). During the execution of the instructionfor a predefined operation on vector/matrix operands, elements ofvector/matrix operands can be operated upon by the Deep LearningAccelerator (DLA) in parallel to reduce execution time and/or energyconsumption associated with memory/data access. The operations onvector/matrix operands of the granularity of the Deep LearningAccelerator (DLA) can be used as building blocks to implementcomputations on vectors/matrices of larger sizes.

The implementation of a typical/practical Artificial Neural Network(ANN) involves vector/matrix operands having sizes that are larger thanthe operation granularity of the Deep Learning Accelerator (DLA). Toimplement such an Artificial Neural Network (ANN) using the DeepLearning Accelerator (DLA), computations involving the vector/matrixoperands of large sizes can be broken down to the computations ofvector/matrix operands of the granularity of the Deep LearningAccelerator (DLA). The Deep Learning Accelerator (DLA) can be programmedvia instructions to carry out the computations involving largevector/matrix operands. For example, atomic computation capabilities ofthe Deep Learning Accelerator (DLA) in manipulating vectors and matricesof the granularity of the Deep Learning Accelerator (DLA) in response toinstructions can be programmed to implement computations in anArtificial Neural Network (ANN).

In some implementations, the Deep Learning Accelerator (DLA) lacks someof the logic operation capabilities of a typical Central Processing Unit(CPU). However, the Deep Learning Accelerator (DLA) can be configuredwith sufficient logic units to process the input data provided to anArtificial Neural Network (ANN) and generate the output of theArtificial Neural Network (ANN) according to a set of instructionsgenerated for the Deep Learning Accelerator (DLA). Thus, the DeepLearning Accelerator (DLA) can perform the computation of an ArtificialNeural Network (ANN) with little or no help from a Central ProcessingUnit (CPU) or another processor. Optionally, a conventional generalpurpose processor can also be configured as part of the Deep LearningAccelerator (DLA) to perform operations that cannot be implementedefficiently using the vector/matrix processing units of the DeepLearning Accelerator (DLA), and/or that cannot be performed by thevector/matrix processing units of the Deep Learning Accelerator (DLA).

A typical Artificial Neural Network (ANN) can be described/specified ina standard format (e.g., Open Neural Network Exchange (ONNX)). Acompiler can be used to convert the description of the Artificial NeuralNetwork (ANN) into a set of instructions for the Deep LearningAccelerator (DLA) to perform calculations of the Artificial NeuralNetwork (ANN). The compiler can optimize the set of instructions toimprove the performance of the Deep Learning Accelerator (DLA) inimplementing the Artificial Neural Network (ANN).

The Deep Learning Accelerator (DLA) can have local memory, such asregisters, buffers and/or caches, configured to store vector/matrixoperands and the results of vector/matrix operations. Intermediateresults in the registers can be pipelined/shifted in the Deep LearningAccelerator (DLA) as operands for subsequent vector/matrix operations toreduce time and energy consumption in accessing memory/data and thusspeed up typical patterns of vector/matrix operations in implementing atypical Artificial Neural Network (ANN). The capacity of registers,buffers and/or caches in the Deep Learning Accelerator (DLA) istypically insufficient to hold the entire data set for implementing thecomputation of a typical Artificial Neural Network (ANN). Thus, a randomaccess memory coupled to the Deep Learning Accelerator (DLA) isconfigured to provide an improved data storage capability forimplementing a typical Artificial Neural Network (ANN). For example, theDeep Learning Accelerator (DLA) loads data and instructions from therandom access memory and stores results back into the random accessmemory.

The communication bandwidth between the Deep Learning Accelerator (DLA)and the random access memory is configured to optimize or maximize theutilization of the computation power of the Deep Learning Accelerator(DLA). For example, high communication bandwidth can be provided betweenthe Deep Learning Accelerator (DLA) and the random access memory suchthat vector/matrix operands can be loaded from the random access memoryinto the Deep Learning Accelerator (DLA) and results stored back intothe random access memory in a time period that is approximately equal tothe time for the Deep Learning Accelerator (DLA) to perform thecomputations on the vector/matrix operands. The granularity of the DeepLearning Accelerator (DLA) can be configured to increase the ratiobetween the amount of computations performed by the Deep LearningAccelerator (DLA) and the size of the vector/matrix operands such thatthe data access traffic between the Deep Learning Accelerator (DLA) andthe random access memory can be reduced, which can reduce therequirement on the communication bandwidth between the Deep LearningAccelerator (DLA) and the random access memory. Thus, the bottleneck indata/memory access can be reduced or eliminated.

The random access memory can have multiple portions configured toreceive sensor data from multiple sensors respectively. The randomaccess memory includes a further portion configured to storeinstructions for the Deep Learning Accelerator (DLA). The instructionshave matrix operands and configured to be executed by the Deep LearningAccelerator (DLA) to implement matrix computations of the ArtificialNeural Networks (ANNs). The instructions can be generated by a compilerfrom descriptions of Artificial Neural Networks (ANNs) to process thesensor data of the multiple sensors. At least one of the ArtificialNeural Networks (ANNs) is trained to generate outputs based on thesensor data of more than one of the sensors. The instructions can beoptimized by the compiler by reducing or eliminating overlappingprocessing in the Artificial Neural Networks (ANNs) and/or bycoordinating the timing of intermediate results derived separately fromdifferent sensors. The sensor fusion results generated from theArtificial Neural Networks (ANNs) combining the input data from multiplesensors can power higher-level intelligent predictions.

Sensor fusion uses inputs from multiple sensors to generate inferences.For example, radars, lidars, cameras sensing in lights visible to humaneyes and/or infrared light, and/or imaging devices sensing viaultrasound can be used to generate image data representative of a sceneof objects and/or items using different technologies and/or differentfrequency ranges. Radar images, lidar images, camera images, andultrasound images can each have advantages and disadvantages underdifferent conditions. Object identification and/or recognition based onthe combination of radar images, lidar images, camera images, and/orultrasound images can be more accurate by taking advantages of thestrengths of the different sensors and the additional informationoffered by different sensors.

Different Artificial Neural Networks can be used to process the inputdata of different image sensors separately. A further Artificial NeuralNetwork can be used to process the input data of the different imagesensors together. The further Artificial Neural Network can receive, asinput, intermediate processing results from the different ArtificialNeural Networks configured for the different image sensors respectively.A combination of the Artificial Neural Networks can be compiled into alarge Artificial Neural Network that generates not only inferencesseparately from the inputs of the individual image sensors, but alsooutputs derived from the sensor data of the image sensors as a whole.Less accurate outputs from the sensor data of the individual imagesensors can be generated sooner than more accurate output from thecombined sensor data of the image sensors. In some instances, theoutputs (e.g., features, identifications and/or classifications)recognized from the sensor data of one of the image sensors can be usedto simplify or assist the processing of the sensor data of other imagesensors.

When the description of the various Neural Networks are provided asinputs to a compiler, the compiler can generate a combined set ofoptimized instructions by reducing overlapping computations and/orcoordinate the timing of the generation of the outputs from thedifferent Artificial Neural Networks.

Thus, the Artificial Neural Networks are compiled into a single set ofinstructions and resources for computation in a single integratedcircuit device having the Deep Learning Accelerator and random accessmemory. The resources can include matrices/parameters representative ofthe synaptic weights, biases, connectivity, and/or other parameters ofartificial neurons.

Alternatively, the Artificial Neural Networks are compiled into multiplesets of instructions and resources for computation in multipleintegrated circuit devices, each having a Deep Learning Accelerator andrandom access memory.

For example, each integrated circuit device has a Deep LearningAccelerator and random access memory storing a set of instructions andresources to at least implement the computation of an Artificial NeuralNetwork processing the input from a particular sensor. The output of theArtificial Neural Network is independent on the input from othersensors. Further, the integrated circuit devices include theinstructions and resources for the implementation of portions of asensor fusion Artificial Neural Network that generates an output basedon the combined sensor data from the multiple sensors. Intermediateresults from other integrated circuit devices can be used as input tothe portions of the sensor fusion Artificial Neural Network to reducedata communication and/or overlapping computations. Further, thecomputation of a portion of the sensor fusion Artificial Neural Networkcan be dynamically assigned to a selected on in the set of integratedcircuit devices. The assignment can be made based on the current orpredicted workloads of the integrated circuit devices, and/or thecommunication cost of transmitting the intermediate results. Thus, theintegrated circuit devices can generate the sensor fusion output throughcooperation.

FIG. 1 shows an integrated circuit device (101) having a Deep LearningAccelerator (103) and random access memory (105) configured according toone embodiment.

The Deep Learning Accelerator (103) in FIG. 1 includes processing units(111), a control unit (113), and local memory (115). When vector andmatrix operands are in the local memory (115), the control unit (113)can use the processing units (111) to perform vector and matrixoperations in accordance with instructions. Further, the control unit(113) can load instructions and operands from the random access memory(105) through a memory interface (117) and a high speed/bandwidthconnection (119).

The integrated circuit device (101) is configured to be enclosed withinan integrated circuit package with pins or contacts for a memorycontroller interface (107).

The memory controller interface (107) is configured to support astandard memory access protocol such that the integrated circuit device(101) appears to a typical memory controller in a way same as aconventional random access memory device having no Deep LearningAccelerator (DLA) (103). For example, a memory controller external tothe integrated circuit device (101) can access, using a standard memoryaccess protocol through the memory controller interface (107), therandom access memory (105) in the integrated circuit device (101).

The integrated circuit device (101) is configured with a high bandwidthconnection (119) between the random access memory (105) and the DeepLearning Accelerator (DLA) (103) that are enclosed within the integratedcircuit device (101). The bandwidth of the connection (119) is higherthan the bandwidth of the connection (109) between the random accessmemory (105) and the memory controller interface (107).

In one embodiment, both the memory controller interface (107) and thememory interface (117) are configured to access the random access memory(105) via a same set of buses or wires. Thus, the bandwidth to accessthe random access memory (105) is shared between the memory interface(117) and the memory controller interface (107). Alternatively, thememory controller interface (107) and the memory interface (117) areconfigured to access the random access memory (105) via separate sets ofbuses or wires. Optionally, the random access memory (105) can includemultiple sections that can be accessed concurrently via the connection(119). For example, when the memory interface (117) is accessing asection of the random access memory (105), the memory controllerinterface (107) can concurrently access another section of the randomaccess memory (105). For example, the different sections can beconfigured on different integrated circuit dies and/or differentplanes/banks of memory cells; and the different sections can be accessedin parallel to increase throughput in accessing the random access memory(105). For example, the memory controller interface (107) is configuredto access one data unit of a predetermined size at a time; and thememory interface (117) is configured to access multiple data units, eachof the same predetermined size, at a time.

In one embodiment, the random access memory (105) and the integratedcircuit device (101) are configured on different integrated circuit diesconfigured within a same integrated circuit package. Further, the randomaccess memory (105) can be configured on one or more integrated circuitdies that allows parallel access of multiple data elements concurrently.

In some implementations, the number of data elements of a vector ormatrix that can be accessed in parallel over the connection (119)corresponds to the granularity of the Deep Learning Accelerator (DLA)operating on vectors or matrices. For example, when the processing units(111) can operate on a number of vector/matrix elements in parallel, theconnection (119) is configured to load or store the same number, ormultiples of the number, of elements via the connection (119) inparallel.

Optionally, the data access speed of the connection (119) can beconfigured based on the processing speed of the Deep LearningAccelerator (DLA) (103). For example, after an amount of data andinstructions have been loaded into the local memory (115), the controlunit (113) can execute an instruction to operate on the data using theprocessing units (111) to generate output. Within the time period ofprocessing to generate the output, the access bandwidth of theconnection (119) allows the same amount of data and instructions to beloaded into the local memory (115) for the next operation and the sameamount of output to be stored back to the random access memory (105).For example, while the control unit (113) is using a portion of thelocal memory (115) to process data and generate output, the memoryinterface (117) can offload the output of a prior operation into therandom access memory (105) from, and load operand data and instructionsinto, another portion of the local memory (115). Thus, the utilizationand performance of the Deep Learning Accelerator (DLA) are notrestricted or reduced by the bandwidth of the connection (119).

The random access memory (105) can be used to store the model data of anArtificial Neural Network (ANN) and to buffer input data for theArtificial Neural Network (ANN). The model data does not changefrequently. The model data can include the output generated by acompiler for the Deep Learning Accelerator (DLA) to implement theArtificial Neural Network (ANN). The model data typically includesmatrices used in the description of the Artificial Neural Network (ANN)and instructions generated for the Deep Learning Accelerator (DLA) (103)to perform vector/matrix operations of the Artificial Neural Network(ANN) based on vector/matrix operations of the granularity of the DeepLearning Accelerator (DLA) (103). The instructions operate not only onthe vector/matrix operations of the Artificial Neural Network (ANN), butalso on the input data for the Artificial Neural Network (ANN).

In one embodiment, when the input data is loaded or updated in therandom access memory (105), the control unit (113) of the Deep LearningAccelerator (DLA) (103) can automatically execute the instructions forthe Artificial Neural Network (ANN) to generate an output of theArtificial Neural Network (ANN). The output is stored into a predefinedregion in the random access memory (105). The Deep Learning Accelerator(DLA) (103) can execute the instructions without help from a CentralProcessing Unit (CPU). Thus, communications for the coordination betweenthe Deep Learning Accelerator (DLA) (103) and a processor outside of theintegrated circuit device (101) (e.g., a Central Processing Unit (CPU))can be reduced or eliminated.

Optionally, the logic circuit of the Deep Learning Accelerator (DLA)(103) can be implemented via Complementary Metal Oxide Semiconductor(CMOS). For example, the technique of CMOS Under the Array (CUA) ofmemory cells of the random access memory (105) can be used to implementthe logic circuit of the Deep Learning Accelerator (DLA) (103),including the processing units (111) and the control unit (113).Alternatively, the technique of CMOS in the Array of memory cells of therandom access memory (105) can be used to implement the logic circuit ofthe Deep Learning Accelerator (DLA) (103).

In some implementations, the Deep Learning Accelerator (DLA) (103) andthe random access memory (105) can be implemented on separate integratedcircuit dies and connected using Through-Silicon Vias (TSV) forincreased data bandwidth between the Deep Learning Accelerator (DLA)(103) and the random access memory (105). For example, the Deep LearningAccelerator (DLA) (103) can be formed on an integrated circuit die of aField-Programmable Gate Array (FPGA) or Application Specific Integratedcircuit (ASIC).

Alternatively, the Deep Learning Accelerator (DLA) (103) and the randomaccess memory (105) can be configured in separate integrated circuitpackages and connected via multiple point-to-point connections on aprinted circuit board (PCB) for parallel communications and thusincreased data transfer bandwidth.

The random access memory (105) can be volatile memory or non-volatilememory, or a combination of volatile memory and non-volatile memory.Examples of non-volatile memory include flash memory, memory cellsformed based on negative- and (NAND) logic gates, negative-or (NOR)logic gates, Phase-Change Memory (PCM), magnetic memory (MRAM),resistive random-access memory, cross point storage and memory devices.A cross point memory device can use transistor-less memory elements,each of which has a memory cell and a selector that are stacked togetheras a column. Memory element columns are connected via two lays of wiresrunning in perpendicular directions, where wires of one lay run in onedirection in the layer that is located above the memory element columns,and wires of the other lay run in another direction and are locatedbelow the memory element columns. Each memory element can beindividually selected at a cross point of one wire on each of the twolayers. Cross point memory devices are fast and non-volatile and can beused as a unified memory pool for processing and storage. Furtherexamples of non-volatile memory include Read-Only Memory (ROM),Programmable Read-Only Memory (PROM), Erasable Programmable Read-OnlyMemory (EPROM) and Electronically Erasable Programmable Read-Only Memory(EEPROM) memory, etc. Examples of volatile memory include DynamicRandom-Access Memory (DRAM) and Static Random-Access Memory (SRAM).

For example, non-volatile memory can be configured to implement at leasta portion of the random access memory (105). The non-volatile memory inthe random access memory (105) can be used to store the model data of anArtificial Neural Network (ANN). Thus, after the integrated circuitdevice (101) is powered off and restarts, it is not necessary to reloadthe model data of the Artificial Neural Network (ANN) into theintegrated circuit device (101). Further, the non-volatile memory can beprogrammable/rewritable. Thus, the model data of the Artificial NeuralNetwork (ANN) in the integrated circuit device (101) can be updated orreplaced to implement an update Artificial Neural Network (ANN), oranother Artificial Neural Network (ANN).

The processing units (111) of the Deep Learning Accelerator (DLA) (103)can include vector-vector units, matrix-vector units, and/ormatrix-matrix units. Examples of units configured to perform forvector-vector operations, matrix-vector operations, and matrix-matrixoperations are discussed below in connection with FIGS. 2-4.

FIG. 2 shows a processing unit configured to perform matrix-matrixoperations according to one embodiment. For example, the matrix-matrixunit (121) of FIG. 2 can be used as one of the processing units (111) ofthe Deep Learning Accelerator (DLA) (103) of FIG. 1.

In FIG. 2, the matrix-matrix unit (121) includes multiple kernel buffers(131 to 133) and multiple the maps banks (151 to 153). Each of the mapsbanks (151 to 153) stores one vector of a matrix operand that hasmultiple vectors stored in the maps banks (151 to 153) respectively; andeach of the kernel buffers (131 to 133) stores one vector of anothermatrix operand that has multiple vectors stored in the kernel buffers(131 to 133) respectively. The matrix-matrix unit (121) is configured toperform multiplication and accumulation operations on the elements ofthe two matrix operands, using multiple matrix-vector units (141 to 143)that operate in parallel.

A crossbar (123) connects the maps banks (151 to 153) to thematrix-vector units (141 to 143). The same matrix operand stored in themaps bank (151 to 153) is provided via the crossbar (123) to each of thematrix-vector units (141 to 143); and the matrix-vector units (141 to143) receives data elements from the maps banks (151 to 153) inparallel. Each of the kernel buffers (131 to 133) is connected to arespective one in the matrix-vector units (141 to 143) and provides avector operand to the respective matrix-vector unit. The matrix-vectorunits (141 to 143) operate concurrently to compute the operation of thesame matrix operand, stored in the maps banks (151 to 153) multiplied bythe corresponding vectors stored in the kernel buffers (131 to 133). Forexample, the matrix-vector unit (141) performs the multiplicationoperation on the matrix operand stored in the maps banks (151 to 153)and the vector operand stored in the kernel buffer (131), while thematrix-vector unit (143) is concurrently performing the multiplicationoperation on the matrix operand stored in the maps banks (151 to 153)and the vector operand stored in the kernel buffer (133).

Each of the matrix-vector units (141 to 143) in FIG. 2 can beimplemented in a way as illustrated in FIG. 3.

FIG. 3 shows a processing unit configured to perform matrix-vectoroperations according to one embodiment. For example, the matrix-vectorunit (141) of FIG. 3 can be used as any of the matrix-vector units inthe matrix-matrix unit (121) of FIG. 2.

In FIG. 3, each of the maps banks (151 to 153) stores one vector of amatrix operand that has multiple vectors stored in the maps banks (151to 153) respectively, in a way similar to the maps banks (151 to 153) ofFIG. 2. The crossbar (123) in FIG. 3 provides the vectors from the mapsbanks (151) to the vector-vector units (161 to 163) respectively. A samevector stored in the kernel buffer (131) is provided to thevector-vector units (161 to 163).

The vector-vector units (161 to 163) operate concurrently to compute theoperation of the corresponding vector operands, stored in the maps banks(151 to 153) respectively, multiplied by the same vector operand that isstored in the kernel buffer (131). For example, the vector-vector unit(161) performs the multiplication operation on the vector operand storedin the maps bank (151) and the vector operand stored in the kernelbuffer (131), while the vector-vector unit (163) is concurrentlyperforming the multiplication operation on the vector operand stored inthe maps bank (153) and the vector operand stored in the kernel buffer(131).

When the matrix-vector unit (141) of FIG. 3 is implemented in amatrix-matrix unit (121) of FIG. 2, the matrix-vector unit (141) can usethe maps banks (151 to 153), the crossbar (123) and the kernel buffer(131) of the matrix-matrix unit (121).

Each of the vector-vector units (161 to 163) in FIG. 3 can beimplemented in a way as illustrated in FIG. 4.

FIG. 4 shows a processing unit configured to perform vector-vectoroperations according to one embodiment. For example, the vector-vectorunit (161) of FIG. 4 can be used as any of the vector-vector units inthe matrix-vector unit (141) of FIG. 3.

In FIG. 4, the vector-vector unit (161) has multiple multiply-accumulateunits (171 to 173). Each of the multiply-accumulate units (e.g., 173)can receive two numbers as operands, perform multiplication of the twonumbers, and add the result of the multiplication to a sum maintained inthe multiply-accumulate (MAC) unit.

Each of the vector buffers (181 and 183) stores a list of numbers. Apair of numbers, each from one of the vector buffers (181 and 183), canbe provided to each of the multiply-accumulate units (171 to 173) asinput. The multiply-accumulate units (171 to 173) can receive multiplepairs of numbers from the vector buffers (181 and 183) in parallel andperform the multiply-accumulate (MAC) operations in parallel. Theoutputs from the multiply-accumulate units (171 to 173) are stored intothe shift register (175); and an accumulator (177) computes the sum ofthe results in the shift register (175).

When the vector-vector unit (161) of FIG. 4 is implemented in amatrix-vector unit (141) of FIG. 3, the vector-vector unit (161) can usea maps bank (e.g., 151 or 153) as one vector buffer (181), and thekernel buffer (131) of the matrix-vector unit (141) as another vectorbuffer (183).

The vector buffers (181 and 183) can have a same length to store thesame number/count of data elements. The length can be equal to, or themultiple of, the count of multiply-accumulate units (171 to 173) in thevector-vector unit (161). When the length of the vector buffers (181 and183) is the multiple of the count of multiply-accumulate units (171 to173), a number of pairs of inputs, equal to the count of themultiply-accumulate units (171 to 173), can be provided from the vectorbuffers (181 and 183) as inputs to the multiply-accumulate units (171 to173) in each iteration; and the vector buffers (181 and 183) feed theirelements into the multiply-accumulate units (171 to 173) throughmultiple iterations.

In one embodiment, the communication bandwidth of the connection (119)between the Deep Learning Accelerator (DLA) (103) and the random accessmemory (105) is sufficient for the matrix-matrix unit (121) to useportions of the random access memory (105) as the maps banks (151 to153) and the kernel buffers (131 to 133).

In another embodiment, the maps banks (151 to 153) and the kernelbuffers (131 to 133) are implemented in a portion of the local memory(115) of the Deep Learning Accelerator (DLA) (103). The communicationbandwidth of the connection (119) between the Deep Learning Accelerator(DLA) (103) and the random access memory (105) is sufficient to load,into another portion of the local memory (115), matrix operands of thenext operation cycle of the matrix-matrix unit (121), while thematrix-matrix unit (121) is performing the computation in the currentoperation cycle using the maps banks (151 to 153) and the kernel buffers(131 to 133) implemented in a different portion of the local memory(115) of the Deep Learning Accelerator (DLA) (103).

FIG. 5 shows a Deep Learning Accelerator and random access memoryconfigured to autonomously apply inputs to a trained Artificial NeuralNetwork according to one embodiment.

An Artificial Neural Network (ANN) (201) that has been trained throughmachine learning (e.g., deep learning) can be described in a standardformat (e.g., Open Neural Network Exchange (ONNX)). The description ofthe trained Artificial Neural Network (ANN) (201) in the standard formatidentifies the properties of the artificial neurons and theirconnectivity.

In FIG. 5, a Deep Learning Accelerator (DLA) compiler (203) convertstrained Artificial Neural Network (ANN) (201) by generating instructions(205) for a Deep Learning Accelerator (DLA) (103) and matrices (207)corresponding to the properties of the artificial neurons and theirconnectivity. The instructions (205) and the matrices (207) generated bythe DLA compiler (203) from the trained Artificial Neural Network (ANN)(201) can be stored in random access memory (105) for the Deep LearningAccelerator (DLA) (103).

For example, the random access memory (105) and the Deep LearningAccelerator (DLA) (103) can be connected via a high bandwidth connection(119) in a way as in the integrated circuit device (101) of FIG. 1. Theautonomous computation of FIG. 5 based on the instructions (205) and thematrices (207) can be implemented in the integrated circuit device (101)of FIG. 1. Alternatively, the random access memory (105) and the DeepLearning Accelerator (DLA) (103) can be configured on a printed circuitboard with multiple point to point serial buses running in parallel toimplement the connection (119).

In FIG. 5, after the results of the DLA compiler (203) are stored in therandom access memory (105), the application of the trained ArtificialNeural Network (ANN) (201) to process an input (211) to the trainedArtificial Neural Network (ANN) (201) to generate the correspondingoutput (213) of the trained Artificial Neural Network (ANN) (201) can betriggered by the presence of the input (211) in the random access memory(105), or another indication provided in the random access memory (105).

In response, the Deep Learning Accelerator (DLA) (103) executes theinstructions (205) to combine the input (211) and the matrices (207).The execution of the instructions (205) can include the generation ofmaps matrices for the maps banks (151 to 153) of one or morematrix-matrix units (e.g., 121) of the Deep Learning Accelerator (DLA)(103).

In some embodiments, the inputs to Artificial Neural Network (ANN) (201)is in the form of an initial maps matrix. Portions of the initial mapsmatrix can be retrieved from the random access memory (105) as thematrix operand stored in the maps banks (151 to 153) of a matrix-matrixunit (121). Alternatively, the DLA instructions (205) also includeinstructions for the Deep Learning Accelerator (DLA) (103) to generatethe initial maps matrix from the input (211).

According to the DLA instructions (205), the Deep Learning Accelerator(DLA) (103) loads matrix operands into the kernel buffers (131 to 133)and maps banks (151 to 153) of its matrix-matrix unit (121). Thematrix-matrix unit (121) performs the matrix computation on the matrixoperands. For example, the DLA instructions (205) break down matrixcomputations of the trained Artificial Neural Network (ANN) (201)according to the computation granularity of the Deep LearningAccelerator (DLA) (103) (e.g., the sizes/dimensions of matrices thatloaded as matrix operands in the matrix-matrix unit (121)) and appliesthe input feature maps to the kernel of a layer of artificial neurons togenerate output as the input for the next layer of artificial neurons.

Upon completion of the computation of the trained Artificial NeuralNetwork (ANN) (201) performed according to the instructions (205), theDeep Learning Accelerator (DLA) (103) stores the output (213) of theArtificial Neural Network (ANN) (201) at a pre-defined location in therandom access memory (105), or at a location specified in an indicationprovided in the random access memory (105) to trigger the computation.

When the technique of FIG. 5 is implemented in the integrated circuitdevice (101) of FIG. 1, an external device connected to the memorycontroller interface (107) can write the input (211) into the randomaccess memory (105) and trigger the autonomous computation of applyingthe input (211) to the trained Artificial Neural Network (ANN) (201) bythe Deep Learning Accelerator (DLA) (103). After a period of time, theoutput (213) is available in the random access memory (105); and theexternal device can read the output (213) via the memory controllerinterface (107) of the integrated circuit device (101).

For example, a predefined location in the random access memory (105) canbe configured to store an indication to trigger the autonomous executionof the instructions (205) by the Deep Learning Accelerator (DLA) (103).The indication can optionally include a location of the input (211)within the random access memory (105). Thus, during the autonomousexecution of the instructions (205) to process the input (211), theexternal device can retrieve the output generated during a previous runof the instructions (205), and/or store another set of input for thenext run of the instructions (205).

Optionally, a further predefined location in the random access memory(105) can be configured to store an indication of the progress status ofthe current run of the instructions (205). Further, the indication caninclude a prediction of the completion time of the current run of theinstructions (205) (e.g., estimated based on a prior run of theinstructions (205)). Thus, the external device can check the completionstatus at a suitable time window to retrieve the output (213).

In some embodiments, the random access memory (105) is configured withsufficient capacity to store multiple sets of inputs (e.g., 211) andoutputs (e.g., 213). Each set can be configured in a predeterminedslot/area in the random access memory (105).

The Deep Learning Accelerator (DLA) (103) can execute the instructions(205) autonomously to generate the output (213) from the input (211)according to matrices (207) stored in the random access memory (105)without helps from a processor or device that is located outside of theintegrated circuit device (101).

In a method according to one embodiment, random access memory (105) of acomputing device (e.g., 101) can be accessed using an interface (107) ofthe computing device (e.g., 101) to a memory controller. The computingdevice (e.g., 101) can have processing units (e.g., 111) configured toperform at least computations on matrix operands, such as a matrixoperand stored in maps banks (151 to 153) and a matrix operand stored inkernel buffers (131 to 133).

For example, the computing device (e.g., 101) can be enclosed within anintegrated circuit package; and a set of connections can connect theinterface (107) to the memory controller that is located outside of theintegrated circuit package.

Instructions (205) executable by the processing units (e.g., 111) can bewritten into the random access memory (105) through the interface (107).

Matrices (207) of an Artificial Neural Network (201) can be written intothe random access memory (105) through the interface (107). The matrices(207) identify the property and/or state of the Artificial NeuralNetwork (201).

Optionally, at least a portion of the random access memory (105) isnon-volatile and configured to store the instructions (205) and thematrices (07) of the Artificial Neural Network (201).

First input (211) to the Artificial Neural Network can be written intothe random access memory (105) through the interface (107).

An indication is provided in the random access memory (105) to cause theprocessing units (111) to start execution of the instructions (205). Inresponse to the indication, the processing units (111) execute theinstructions to combine the first input (211) with the matrices (207) ofthe Artificial Neural Network (201) to generate first output (213) fromthe Artificial Neural Network (201) and store the first output (213) inthe random access memory (105).

For example, the indication can be an address of the first input (211)in the random access memory (105); and the indication can be stored apredetermined location in the random access memory (105) to cause theinitiation of the execution of the instructions (205) for the input(211) identified by the address. Optionally, the indication can alsoinclude an address for storing the output (213).

The first output (213) can be read, through the interface (107), fromthe random access memory (105).

For example, the computing device (e.g., 101) can have a Deep LearningAccelerator (103) formed on a first integrated circuit die and therandom access memory (105) formed on one or more second integratedcircuit dies. The connection (119) between the first integrated circuitdie and the one or more second integrated circuit dies can includeThrough-Silicon Vias (TSVs) to provide high bandwidth for memory access.

For example, a description of the Artificial Neural Network (201) can beconverted using a compiler (203) into the instructions (205) and thematrices (207). The combination of the instructions (205) and thematrices (207) stored in the random access memory (105) and the DeepLearning Accelerator (103) provides an autonomous implementation of theArtificial Neural Network (201) that can automatically convert input(211) to the Artificial Neural Network (201) to its output (213).

For example, during a time period in which the Deep Learning Accelerator(103) executes the instructions (205) to generate the first output (213)from the first input (211) according to the matrices (207) of theArtificial Neural Network (201), the second input to Artificial NeuralNetwork (201) can be written into the random access memory (105) throughthe interface (107) at an alternative location. After the first output(213) is stored in the random access memory (105), an indication can beprovided in the random access memory to cause the Deep LearningAccelerator (103) to again start the execution of the instructions andgenerate second output from the second input.

During the time period in which the Deep Learning Accelerator (103)executes the instructions (205) to generate the second output from thesecond input according to the matrices (207) of the Artificial NeuralNetwork (201), the first output (213) can be read from the random accessmemory (105) through the interface (107); and a further input can bewritten into the random access memory to replace the first input (211),or written at a different location. The process can be repeated for asequence of inputs.

The Deep Learning Accelerator (103) can include at least onematrix-matrix unit (121) that can execute an instruction on two matrixoperands. The two matrix operands can be a first matrix and a secondmatrix. Each of two matrices has a plurality of vectors. Thematrix-matrix unit (121) can include a plurality of matrix-vector units(141 to 143) configured to operate in parallel. Each of thematrix-vector units (141 to 143) are configured to operate, in parallelwith other matrix-vector units, on the first matrix and one vector fromsecond matrix. Further, each of the matrix-vector units (141 to 143) canhave a plurality of vector-vector units (161 to 163) configured tooperate in parallel. Each of the vector-vector units (161 to 163) isconfigured to operate, in parallel with other vector-vector units, on avector from the first matrix and a common vector operand of thecorresponding matrix-vector unit. Further, each of the vector-vectorunits (161 to 163) can have a plurality of multiply-accumulate units(171 to 173) configured to operate in parallel.

The Deep Learning Accelerator (103) can have local memory (115) and acontrol unit (113) in addition to the processing units (111). Thecontrol unit (113) can load instructions (205) and matrix operands(e.g., matrices (207)) from the random access memory (105) for executionby the processing units (111). The local memory can cache matrixoperands used by the matrix-matrix unit. The connection (119) can beconfigured with a bandwidth sufficient to load a set of matrix operandsfrom the random access memory (105) to the local memory (115) during atime period in which the matrix-matrix unit performs operations on twoother matrix operands. Further, during the time period, the bandwidth issufficient to store a result, generated by the matrix-matrix unit (121)in a prior instruction execution, from the local memory (115) to therandom access memory (105).

FIGS. 6-8 illustrate sensor fusion implemented in a Deep LearningAccelerator and random access memory configured according to someembodiments.

In FIG. 6, the Random Access Memory (RAM) (105) includes a portionconfigured to accept the input (211) to the Artificial Neural Network(ANN) (201). The portion of the Random Access Memory (RAM) (105) ispartitioned into a plurality of slots for accepting inputs fromdifferent types of sensors, such as radar, lidar, camera, and/orultrasound imaging device.

For example, each of the slots can be configured to accept an input froma predefined type of sensors. For example, a slot of the input (211) tothe Artificial Neural Network (ANN) (201) can be reserved for the input(221) from sensor A (e.g., representative of radar images or lidarimages); and another slot of the input (211) to the Artificial NeuralNetwork (ANN) (201) can be reserved for the input (223) from sensor B(e.g., representative of camera images or ultrasound images).

In some implementations, which slot is used for the sensor data of whattype is dynamically specified in the Random Access Memory (RAM) (105).For example, a slot in the Random Access Memory (RAM) (105) can beinitially used to store an input (221) from sensor A (e.g.,representative of radar images or lidar images) and subsequentlyreallocated to store an input (223) from sensor B (e.g., representativeof camera images or ultrasound images). For example, the input (221)from sensor A can include data identifying its type of input (e.g.,radar image stream); and the input (223) from sensor B can include dataidentifying its type of input (e.g., camera image stream).

The DLA instructions (205) generated by the DLA compiler (203) can beconfigured to dynamically apply the sensor inputs (e.g., 221) to thecorresponding input neurons of the Artificial Neural Network (ANN) (201)based on the types of the inputs (e.g., 221) specified for the slot. Theinput neurons correspond to predetermined portions of the matrices(207), as identified via the DLA instructions (205).

Alternatively, the input slots in Random Access Memory (RAM) (105) canbe pre-allocated for predetermined types of sensors. For example, duringa startup process, the types of sensors connected to the sensorinterface(s) (227) of integrated circuit device (101) are identified.The slots are allocated respectively for the sensors to store theirinputs (e.g., 223).

The output (213) from the Artificial Neural Network (ANN) (201) caninclude outputs derived from the inputs (221, . . . , 223) respectivelyfor the different sensors, such as outputs recognized from radar images,outputs recognized from lidar images, output recognized from cameraimages, etc.

Further, the output (213) from the Artificial Neural Network (ANN) (201)can include an output generated from the sensor fusion of the inputs(221, . . . , 223), such as identifications or classifications of one ormore objects recognized from a combination of radar images, lidarimages, camera images, and/or ultrasound images, etc.

For example, the execution of the DLA instructions (205) generates radarimage features, lidar image features, camera image features. Further,the execution of the DLA instructions (205) generates sensor fusionfeatures identified or recognized based on a combination of the radarimage features, the lidar image features, the camera image features,etc.

For example, the execution of the DLA instructions (205) generatesidentifications of an object determined from a radar image, a lidarimage, a camera image respectively. The radar identification of theobject is determined from the radar image features; the lidaridentification of the object is determined from the lidar imagefeatures; and the camera identification is determined from the cameraimage features. Further, the execution of the DLA instructions (205)generates a sensor fusion identification of the object that isidentified or recognized based on a combination of a radar image, alidar image, a camera image, etc. For example, the sensor fusionidentification of the object can be determined from a combination ofradar image features, lidar image features, camera image features, etc.

In FIG. 6, the different sensors write the inputs (221, . . . , 223)into the Random Access Memory (RAM) (105) using a memory controllerinterface (107). For example, a host system, a processor, or a directmemory access (DMA) controller can use the memory controller interface(107) to store the inputs (221, . . . , 223) into the respective slotsin the Random Access Memory (RAM) (105) on behalf of the differentsensors. The host system or the processor can use the memory controllerinterface (107) to retrieve the output (213) from the Artificial NeuralNetwork (ANN) (201).

Alternatively, one or more sensor interfaces can be provided to allowone or more sensors to stream inputs (e.g., 221) into the Random AccessMemory (RAM) (105). The sensor interfaces can be used independent on thehost system/processor using the memory controller interface (107) toaccess the output (213) from the Artificial Neural Network (ANN) (201),as illustrated in FIG. 7.

In FIG. 7, one or more sensor interfaces (227) are provided to allow oneor more sensor devices to write inputs (221, . . . , 223) into theRandom Access Memory (RAM) (105). For example, radar, lidar, and acamera can write parallel streams of radar images, lidar images andcamera images into the Random Access Memory (RAM) (105). For example,one sensor (e.g., radar) can use a serial connection to a dedicatedsensor interface (e.g., 227) to write its input (e.g., 221) into theRandom Access Memory (RAM) (105); and another sensor (e.g., camera orlidar) can use another serial connection to another dedicated interfaceto write its input (e.g., 223) into the Random Access Memory (RAM)(105). The inputs (e.g., radar images and camera images) can be writteninto the Random Access Memory (RAM) (105) concurrently.

FIG. 7 illustrates an example in which the connections (109 and 229)connect the memory controller interface (107) and the sensorinterface(s) (227) to the Random Access Memory (RAM) (105) directly.Alternatively, the connection (109) and the connection (229) can beconfigured to connect the memory controller interface (107) and thesensor interface(s) (227) to the Random Access Memory (RAM) (105)indirectly through the memory interface (117) and/or the high bandwidthconnection (119) between the Deep Learning Accelerator (DLA) (103) andthe Random Access Memory (RAM) (105).

The integrated circuit device (101) of FIG. 8 includes a CentralProcessing Unit (CPU) (225). The Central Processing Unit (CPU) (225) canexecute instructions like a typical host system/processor. Thus, theRandom Access Memory (RAM) (105) can store not only DLA instructions(205) for execution by Deep Learning Accelerator (DLA) (103), but alsoinstructions of an application (235) for execution by the CentralProcessing Unit (CPU) (225).

The integrated circuit device (101) of FIG. 8 has one or moreinput/output interfaces (237). Sensors (231, . . . , 233) can streamtheir inputs into the Random Access Memory (RAM) (105) through the oneor more input/output interfaces (237). For example, the sensor A (231)can stream its input (221) from sensor A (e.g., representative of radarimages or camera images) into the Random Access Memory (RAM) (105)(e.g., to provide radar images or camera images); and the sensor B (233)can stream its input (223) from sensor B into the Random Access Memory(RAM) (105) (e.g., to provide lidar images or ultrasound images).

Further, the application (235) running in the Central Processing Unit(CPU) (225) can use the input/output interfaces (237) to generatealerts, audio/video outputs, network communication signals, and/orcontrol commands for peripheral devices.

FIG. 8 illustrates an example in which the connections (109 and 229)connect the memory controller (228) and the input/output interfaces(237) to the Random Access Memory (RAM) (105) directly. Alternatively,the connection (109) and the connection (229) can be configured toconnect the memory controller (228) and the input/output interface(s)(237) to the Random Access Memory (RAM) (105) indirectly via the memoryinterface (117) and the high bandwidth connection (119) between the DeepLearning Accelerator (DLA) (103) and the Random Access Memory (RAM)(105). In other implementations, the input/output interfaces (237)access the Random Access Memory (RAM) (105) via the memory controller(228), the Central Processing Unit (CPU) (225), or another controller.

For example, the input/output interfaces (237) can be configured tosupport serial connections to peripheral devices, such as radar, lidar,camera, display device, etc. For example, the input/output interfaces(237) can include a peripheral component interconnect express (PCIe)interface, a universal serial bus (USB) interface, a Mobile IndustryProcessor Interface (MIPI), and/or a camera interface, etc.

In one embodiment of sensor fusion, first data representative ofparameters of an artificial neural network (201) is stored into randomaccess memory (105) of a device (e.g., 101). For example, the parameterscan include kernel and maps matrices (207) of the artificial neuralnetwork (201) trained using a machine learning and/or deep learningtechnique.

Second data representative of instructions (e.g., 205) executable toimplement matrix computations of the artificial neural network (201) isstored into the random access memory (105) of the device (e.g., 101).The matrix computations are implemented using at least the first datastored in the random access memory (105).

At least one interface of the device (e.g., 101) receives third datarepresentative of a plurality of inputs from a plurality of sensorsrespectively.

For example, the at least one interface can include the memorycontroller interface (107) illustrated in FIGS. 6-8, sensor interface(s)(227) illustrated in FIG. 7, and/or input/output interfaces (237)illustrated in FIG. 8.

For example, the plurality of inputs can include inputs (221, . . . ,223) illustrated in FIGS. 6-8; and the plurality of sensors can includesensors (231, . . . , 233) illustrated in FIG. 8.

For example, the at least one interface can include a plurality ofinterfaces configured to receive the plurality of inputs (e.g., 221, . .. , 223) from the plurality of sensors (e.g., 231, . . . , 233) inparallel. A plurality of serial connections can connect the plurality ofinterfaces to the plurality of sensors (e.g., 231, . . . , 233)respectively.

The at least one interface stores, into the random access memory (105)of the device, the third data representative of the plurality of inputsfrom the plurality of sensors respectively.

At least one processing unit (111) of the device (e.g., 101) executesthe instructions (e.g., 205) represented by the second data to implementthe matrix computations of the artificial neural network (201) having aplurality of first portions and a second portion.

The device (e.g., 101) generates first outputs corresponding to theplurality of sensors respectively according to the plurality of thefirst portions of the artificial neural network (201) by processing theplurality of inputs from the plurality of sensors respectively.

The device (e.g., 101) generates a second output according to the secondportion of the artificial neural network (201) by processing acombination of the plurality inputs from the plurality of sensors.

For example, an input from each of the plurality of sensors can includeimage data of a same object; the first outputs include identificationsor classifications of the object determined using the plurality ofsensors respectively; and the second output includes an identificationor classification of the object determined using a combination of theplurality of sensors.

For example, the first portions of the artificial neural network (201)generates the first outputs that include features recognized from theplurality of inputs from the plurality of sensors respectively. Thesecond portion of the artificial neural network (201) generates thesecond output that includes an identification or classification of theobject determined using a combination of the features in the firstoutputs.

For example, the plurality of sensors can include at least two imagingdevices, such as a radar imaging device, a lidar imaging device, anultrasound imaging device, a digital camera, etc.

Optionally, the integrated circuit device (101) can further include acentral process unit configured to execute instructions of anapplication (235) stored in the random access memory (105). An interfaceof the device is configured to provide an output of the application(235) generated by the central processing unit (225) based on theoutputs (213) of the artificial neural network (201). For example, theoutput of the application (235) can be provided to a peripheral device,such as a display device, a control element, a computer network, etc.

FIGS. 6-8 illustrate the techniques to implement sensor fusion in asingle integrated circuit device (101).

Alternatively, the processing of sensor data from multiple sensorsand/or sensor fusion computation can be implemented using multipleintegrated circuit device (101).

FIGS. 9-11 illustrate collaborative sensor data processing by DeepLearning Accelerators with random access memory configured according tosome embodiments.

In FIG. 9, a plurality of integrated circuit devices (e.g., 241, . . . ,243) are configured to be connected to separate sensors (e.g., 231, . .. , 233).

Each of the integrated circuit devices (e.g., 241, . . . , 243) caninclude a Deep Learning Accelerator (DLA) (103) and a Random AccessMemory (RAM) (105), in a way similar to the integrated circuit device(101) of FIGS. 1 and 6-8, or the computing system of FIG. 5.

Each of the integrated circuit devices (e.g., 241, . . . , 243) can beconfigured to process the input from its one or more primary sensors.

For example, integrated circuit device A (241) can be configured toreceive inputs from sensor A (231); and integrated circuit device B(243) can be configured to receive inputs from sensor B (233). Theinputs generated by sensor A (231) are not stored into the Random AccessMemory (RAM) (105) of integrated circuit device B (243); and the inputsgenerated by sensor B (233) are not stored into the Random Access Memory(RAM) (105) of integrated circuit device A (241).

The DLA compiler (203) in the host system generates DLA instructions(205) and matrices (207) that are stored in the Random Access Memory(RAM) (105) of each of the integrated circuit devices (e.g., 241, . . ., 243). The DLA compiler (203) can be implemented as a softwareapplication running in the processor(s) (251) in the host system (249)that controls or uses the integrated circuit devices (e.g., 241, . . . ,243).

The Artificial Neural Network (ANN) (201) can include a portion toprocess the input from the sensor A (231). The processing of such aportion is identified to be independent from the inputs from othersensors (e.g., 233). Thus, the DLA compiler (203) can generate, fromsuch a portion of the Artificial Neural Network (ANN) (201), a set ofDLA instructions (205) and matrices (207) for storing in the RandomAccess Memory (RAM) (105) of the integrated circuit device A (241). Theoutput of such a portion can include inferences results of theArtificial Neural Network (ANN) (201) generated using the sensor A (231)without using other sensors (e.g., 233). The output of such a portioncan further include intermediate results (e.g., features,identifications, classifications) that are further processed in one ormore additional sensor fusion portions of the Artificial Neural Network(ANN) (201). A sensor fusion portion of the Artificial Neural Network(ANN) (201) includes computations that are dependent on sensorsconnected to multiple integrated circuit devices (e.g., 241, . . . ,243).

Similarly, the Artificial Neural Network (ANN) (201) can include aportion to process the input from the sensor B (233). The processing ofsuch a portion is identified to be independent from the inputs fromother sensors (e.g., 231). Thus, the DLA compiler (203) can generate,from such a portion of the Artificial Neural Network (ANN) (201), a setof DLA instructions (205) and matrices (207) for storing in the RandomAccess Memory (RAM) (105) of the integrated circuit device B (243). Theoutput of such a portion can include inferences results of theArtificial Neural Network (ANN) (201) generated using the sensor B (233)without using other sensors (e.g., 231). The output of such a portioncan further include intermediate results (e.g., features,identifications, classifications) that are further processed in one ormore additional sensor fusion portions of the Artificial Neural Network(ANN) (201).

A sensor fusion portion of the Artificial Neural Network (ANN) (201)uses inputs from sensors connected different integrated circuit devices(e.g., 241, . . . , 243). The DLA compiler (203) can generate, from sucha sensor fusion portion of the Artificial Neural Network (ANN) (201), aset of DLA instructions (205) and matrices (207) for at least one of theintegrated circuit devices (e.g., 241, . . . , 243).

For example, the sensor fusion portion can include processing based onthe inputs from the sensor A (231) and the sensor B (233). The DLAcompiler (203) generates and stores a set of DLA instructions (205) andmatrices (207) into the Random Access Memory (RAM) (105) of theintegrated circuit device A (241). The DLA instructions (205) can beconfigured to use the intermediate results, generated using sensor A(231) and sensor B (233) in integrated circuit device A (241) andintegrated circuit device B (243) respectively, to generate a sensorfusion output.

Further, the DLA compiler (203) generates and stores a set ofinstructions for the communication of the intermediate results fromintegrated circuit device B (243) to integrated circuit device A (241).For example, the set of instructions can be configured in the integratedcircuit device B (243) to write its intermediate results into the RandomAccess Memory (RAM) (105) of the sensor A (231). Thus, during thecommunication of the intermediate results, the integrated circuit deviceB (243) can function in a way like a virtual sensor that provides theintermediate results computed by the integrated circuit device B (243)from the inputs from the sensor B (233). Alternatively, a set ofinstructions can be configured in the integrated circuit device A (241)to read the intermediate results from the Random Access Memory (RAM)(105) 105 of the sensor B (233). In some embodiments, instructions arestored into both the integrated circuit device A (241) and integratedcircuit device B (243) to coordinate the communication of theintermediate results from the integrated circuit device B (243) to theintegrated circuit device A (241). In other implementations, the hostsystem (249) communicates with the integrated circuit devices (e.g.,241, . . . , 243) to coordinate, initiate, and/or control thecommunication of the intermediate results.

In some implementations, a sensor fusion portion is implemented in morethan one of the integrated circuit devices (e.g., 241, . . . , 243). Forexample, the sensor fusion portion using the sensor A (231) and sensor B(233) can be implemented in both integrated circuit device A (241) andintegrated circuit device B (243). The intermediate results generatedusing the input from the sensor A (231) can be stored into theintegrated circuit device B (243) for a sensor fusion output; and theintermediate results generated using the input from the sensor B (233)can be stored into the integrated circuit device A (241) for a redundantsensor fusion output. Thus, the sensor fusion output can be obtainedfrom either the integrated circuit device A (241) or the integratedcircuit device B (243), whichever generates the sensor fusion outputfirst. In some implementations, the consistency between the redundantoutputs is checked for improved reliability.

Typically, the intermediate results generated by a portion of theArtificial Neural Network (ANN) (201) from the input of a sensor (e.g.,233 or 231) is smaller than the original input of the sensor. Thus,transmitting the intermediate results for sensor fusion can reduce thecomputation load by avoiding overlapping computations and can reduce thecommunication delay and/or the communication bandwidth requirement.

Optionally, the DLA compiler (203) can break the Artificial NeuralNetwork (ANN) (201) to organize multiple sensor fusion portions. Thesensor fusion portions can be distributed to the integrated circuitdevices (e.g., 241, . . . , 243) for optimized performance and loadbalancing.

In some implementations, each of the integrated circuit devices (e.g.,241, . . . , 243) can include the instructions and resources forimplement various portions of the Artificial Neural Network (ANN) (201).The integrated circuit devices (e.g., 241, . . . , 243) can communicatewith each other to dynamically negotiate the transfer of intermediateresults and/or to execute instructions for the processing of selectedportions of the Artificial Neural Network (ANN) (201), as illustrated inFIG. 10.

In FIG. 10, integrated circuit devices (e.g., 241, . . . , 243, . . . ,245) cooperate with each other in implementing an Artificial NeuralNetwork (ANN) (201) that has one or more sensor fusion portions.

For example, the computation of a sensor fusion portion of theArtificial Neural Network (ANN) (201) can be implemented in theintegrated circuit device C (245), which receives intermediate resultsother devices, such as integrated circuit device A (241) and integratedcircuit device B (243).

For example, the integrated circuit device A (241) and integratedcircuit device B (243) can function as high level sensors that generateinputs for a sensor fusion portion of the Artificial Neural Network(ANN) (201) can be implemented in the integrated circuit device C (245).Optionally, a further sensor can be connected to provide inputs to theintegrated circuit device C (245).

Optionally, a portion of the Artificial Neural Network (ANN) (201)implemented in the integrated circuit device C (245) further predictsthe workloads and timings of results from a sensor fusion portion of theArtificial Neural Network (ANN) (201) can be implemented in theintegrated circuit device C (245) and assign the computation task of thesensor fusion portion of the Artificial Neural Network (ANN) (201) toone of the integrated circuit devices (e.g., 241, . . . , 243, . . . ,245).

For example, when the computation task of the sensor fusion portion ofthe Artificial Neural Network (ANN) (201) is assigned to the integratedcircuit device A (241), the integrated circuit device C (245) cancoordinate, initiate, and/or control the transmission of intermediateresults to the integrated circuit device A (241) (e.g., from integratedcircuit device B (243)).

In some implementations, when the computation task of the sensor fusionportion of the Artificial Neural Network (ANN) (201) is assigned to theintegrated circuit device A (241), the integrated circuit device C (245)writes the instructions and the resources for the implementation of thesensor fusion portion into the Random Access Memory (RAM) (105) of theintegrated circuit device A (241). Alternatively, the instructions andthe resources are pre-configured in each of the integrated circuitdevices (e.g., 241, . . . , 243, . . . , 245). The availability of theintermediate results in an integrated circuit device (e.g., 241 or 243)can trigger the execution of the instructions for the implementation ofthe sensor fusion portion.

In some implementations, one of the integrated circuit devices (e.g.,241, . . . , 243, . . . , 245) can be elected to be the controller tocoordinate data flow and execution of the instructions of the sensorfusion portion.

Alternatively, a messaging system is used by the integrated circuitdevices (e.g., 241, . . . , 243, . . . , 245) to announce theavailability of intermediate results, request for the intermediateresults, and/or assign the computation task of the sensor fusionportion.

Optionally, one of the integrated circuit devices (e.g., 241, . . . ,243, . . . , 245) includes a Central Processing Unit (CPU) (225), asillustrated in FIG. 8.

For example, the application (235) running in the Central ProcessingUnit (CPU) (225) can include the DLA compiler (203) for generatingand/or distributing the DLA instructions (205) and matrices (207) to theintegrated circuit devices (e.g., 241, . . . , 243, . . . , 245).

For example, the application (235) running in the Central ProcessingUnit (CPU) (225) can monitor and/or predict the workloads of theintegrated circuit devices (e.g., 241, . . . , 243, . . . , 245) anddynamically assign and/or reassign sensor fusion computation to one ormore of the integrated circuit devices (e.g., 241, . . . , 243, . . . ,245).

In some implementations, a sensor can be integrated inside an integratedcircuit device having a Deep Learning Accelerator (DLA) (103) and RandomAccess Memory (RAM) (105), as illustrated in FIG. 11.

In FIG. 11, integrated circuit devices (e.g., 241, . . . , 243, . . . ,245) are connected via an interconnect (247), such as a network, or abus, or a set of peer to peer connections. A sensor A (231) isintegrated and/or packaged within an integrated circuit device A (241)for improved data communication connection for the inputs of the sensorA (231).

For example, the sensor A (231) can be an image sensor that generates alarge amount of data to be processed by a portion of the ArtificialNeural Network (ANN) (201) to recognize objects, features,identifications and/or classifications in the images from the imagesensor. Thus, the integrated circuit device A (241) can be used alone toprovide inferences results when other sensors (e.g., 233) are notavailable and/or not functional.

For example, the integrated circuit device A (241) can be configured ina digital camera and optionally used with other sensors for sensorfusion. Such a digital camera can be used as an intelligent imagingdevice.

For example, the sensor B (233) can be a radar imaging device; and theintegrated circuit device B (243) can be configured in the radar imagingdevice to provide recognized objects, features, identifications and/orclassifications in the radar image. Such a radar imaging device can beused as an intelligent imaging device.

Similarly, an integrated circuit device (e.g., 101 or 245) having a DeepLearning Accelerator (DLA) (103) with Random Access Memory (RAM) (105)can be configured in an ultrasound imaging device, a lidar device, or adigital camera.

When two or more such intelligent imaging devices are connected, theintegrated circuit device (e.g., 101 or 245) can be further configuredto perform cooperative sensor fusion.

FIG. 12 shows a method of sensor fusion according to one embodiment. Forexample, the method of FIG. 12 can be implemented in the integratedcircuit device (101) of FIG. 1, FIG. 6, FIG. 7, FIG., 8 and/or thesystem of FIG. 5.

At block 301, first data representative of parameters of a first portionof an artificial neural network (201) is stored into random accessmemory (105) of a device (e.g., 101, 241 or 245).

At block 303, second data representative of instructions executable toimplement matrix computations of the first portion the artificial neuralnetwork (201) using at least the first data is stored into the randomaccess memory (105) of the device (e.g., 101, 241, or 245).

At block 305, at least one interface (e.g., 107, 227, and/or 237) of thedevice (e.g., 101, 241, or 245) receives third data from a sensor (e.g.,231) and fourth data generated outside of the device according to asecond portion of the artificial neural network (201).

In some implementations, the sensor (e.g., 231) is configured within thedevice (e.g., as illustrated in FIG. 11). Thus, the data from the sensor(e.g., 231) can be received in the random access memory (105) via aninternal connection (e.g., 119).

At block 307, the least one interface (e.g., 107, 227, and/or 237)stores into the random access memory (105) of the device (e.g., 101,241, or 245), the third data from the sensor (e.g., 231) and the fourthdata generated according to the second portion of the artificial neuralnetwork (201).

At block 309, at least one processing unit (e.g., 111) of the device(e.g., 101, 241, or 245) executes the instructions represented by thesecond data to implement the matrix computations of the first portionthe artificial neural network (201).

At block 311, the device (e.g., 101, 241, or 245) generates a firstoutput independent of the fourth data according to the first portion ofthe artificial neural network (201) by processing the third data fromthe sensor (e.g., 231).

For example, the first output can include an identification orclassification of an object determined independent of the fourth datagenerated outside of the device (e.g., 101, 241, or 145) according tothe second portion of the artificial neural network (201).

At block 313, the device (e.g., 101, 241, or 245) generates a secondoutput according to the first portion of the artificial neural network(e.g., 201) by processing a combination of the third data from thesensor (e.g., 231) and the fourth data generated outside of the device.For example, the fourth data can be generated by another device (243)according to the second portion of the artificial neural network (201)).

For example, the sensor (e.g., 231) configured in or connected to thedevice (e.g., 101, 241, or 245) can be a first sensor (e.g., a firstimaging device). The fourth data can be generated, according to thesecond portion of the artificial neural network, based on an input froma second sensor (e.g., 233) configured outside of the device (e.g., 101,241, or 245). For example, the second sensor (e.g., 233) can beconfigured in or connected to another device (243).

For example, the first sensor (e.g., 231) and the second sensor (e.g.,233) are imaging sensors, such as a radar imaging sensor, a lidarimaging sensor, an ultrasound imaging sensor, an image sensor of adigital camera, an infrared imaging sensor, etc.

For example, the second output can include an identification orclassification of the object determined based on both the first sensor(e.g., 231) and the second sensor (e.g., 233).

Optionally, computation of a third portion of the artificial neuralnetwork (201) is configured outside of the device (e.g., 101, 241, or245); and the at least one interface of the device (e.g., 101, 241, or245) is configured to provide at least a portion of the first output tooutside of the device as an input to the third portion of the artificialneural network (201).

For example, the third portion can be implemented in another device(e.g., 243) to generate an output based on the sensor (231) and anothersensor (e.g., 233). Optionally, the third portion can generate a resultthat is redundant to the second output.

For example, the portion of the first output generated in the device(e.g., 101, 241, or 245) can include features in the third data from thesensor (e.g., 231) recognized via the first portion of the artificialneural network (201). The recognized features have a data size smallerthan the original sensor data from the sensor (e.g., 231). Therecognized features can be transmitted to another device (e.g., 243) togenerate a sensor fusion output based on the sensor (e.g., 231) and oneor more other sensors (e.g., 233).

Optionally, the device (e.g., 101, 241, or 245) is further configured tocommunicate through the at least one interface (e.g., e.g., 107, 227,and/or 237) to identify an external device (e.g., 243) operable toimplement matrix computations of the third portion of the artificialneural network (201) and write the portion of the first output intorandom access memory (105) of the external device (e.g., 243).

Optionally, the device (e.g., 101, 241, or 245) is further configured tocommunicate through the at least one interface (e.g., e.g., 107, 227,and/or 237) to determine whether to perform computation to generate thesecond output. The first output can be generated independent of whetherthe computation to generate the second output is performed.

Optionally, the device (e.g., 101, 241, or 245) is further configured tocommunicate through the at least one interface (e.g., e.g., 107, 227,and/or 237) to identify the external device (e.g., 243) that is operableto supply the fourth data and to obtain or read the fourth data from therandom access memory (105) of the external device (e.g., 243).

Optionally, the device (e.g., 101, 241, or 245) includes a centralprocess unit configured to run an application (235) stored in the randomaccess memory (105). The application (235) can include a compiler (203)configured to partition, based on a description of the artificial neuralnetwork (201), the artificial neural network (201) into portions. Thecompiler (203) can generate, from the description of the artificialneural network (201), data representative of parameters of portions ofthe artificial neural network (201) and data representative ofinstructions executable to implement matrix computations of the portionsof the artificial neural network (201). Further, the application candistribute computations of the portions of the artificial neural networkto a plurality of devices (e.g., 243) by storing the DLA instructions(205) and matrices (207) of the respective portions into the randomaccess memory (105) of the respective devices (e.g., 243).

Optionally, the at least one interface (e.g., e.g., 107, 227, and/or237) of the device (e.g., 101, 241, or 245) can include a plurality ofinterfaces configured to be connected to the plurality of devices (e.g.,243) over a plurality of serial connections respectively. For example,the device (e.g., 101, 241, or 245) running the application (235) candynamically distribute computation tasks of the portions of theartificial neural network to the plurality of devices (e.g., 243) basedon current or predicted workloads of the respective devices (e.g., 243).

The present disclosure includes methods and apparatuses which performthe methods described above, including data processing systems whichperform these methods, and computer readable media containinginstructions which when executed on data processing systems cause thesystems to perform these methods.

A typical data processing system may include an inter-connect (e.g., busand system core logic), which interconnects a microprocessor(s) andmemory. The microprocessor is typically coupled to cache memory.

The inter-connect interconnects the microprocessor(s) and the memorytogether and also interconnects them to input/output (I/O) device(s) viaI/O controller(s). I/O devices may include a display device and/orperipheral devices, such as mice, keyboards, modems, network interfaces,printers, scanners, video cameras and other devices known in the art. Inone embodiment, when the data processing system is a server system, someof the I/O devices, such as printers, scanners, mice, and/or keyboards,are optional.

The inter-connect can include one or more buses connected to one anotherthrough various bridges, controllers and/or adapters. In one embodimentthe I/O controllers include a USB (Universal Serial Bus) adapter forcontrolling USB peripherals, and/or an IEEE-1394 bus adapter forcontrolling IEEE-1394 peripherals.

The memory may include one or more of: ROM (Read Only Memory), volatileRAM (Random Access Memory), and non-volatile memory, such as hard drive,flash memory, etc.

Volatile RAM is typically implemented as dynamic RAM (DRAM) whichrequires power continually in order to refresh or maintain the data inthe memory. Non-volatile memory is typically a magnetic hard drive, amagnetic optical drive, an optical drive (e.g., a DVD RAM), or othertype of memory system which maintains data even after power is removedfrom the system. The non-volatile memory may also be a random accessmemory.

The non-volatile memory can be a local device coupled directly to therest of the components in the data processing system. A non-volatilememory that is remote from the system, such as a network storage devicecoupled to the data processing system through a network interface suchas a modem or Ethernet interface, can also be used.

In the present disclosure, some functions and operations are describedas being performed by or caused by software code to simplifydescription. However, such expressions are also used to specify that thefunctions result from execution of the code/instructions by a processor,such as a microprocessor.

Alternatively, or in combination, the functions and operations asdescribed here can be implemented using special purpose circuitry, withor without software instructions, such as using Application-SpecificIntegrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA).Embodiments can be implemented using hardwired circuitry withoutsoftware instructions, or in combination with software instructions.Thus, the techniques are limited neither to any specific combination ofhardware circuitry and software, nor to any particular source for theinstructions executed by the data processing system.

While one embodiment can be implemented in fully functioning computersand computer systems, various embodiments are capable of beingdistributed as a computing product in a variety of forms and are capableof being applied regardless of the particular type of machine orcomputer-readable media used to actually effect the distribution.

At least some aspects disclosed can be embodied, at least in part, insoftware. That is, the techniques may be carried out in a computersystem or other data processing system in response to its processor,such as a microprocessor, executing sequences of instructions containedin a memory, such as ROM, volatile RAM, non-volatile memory, cache or aremote storage device.

Routines executed to implement the embodiments may be implemented aspart of an operating system or a specific application, component,program, object, module or sequence of instructions referred to as“computer programs.” The computer programs typically include one or moreinstructions set at various times in various memory and storage devicesin a computer, and that, when read and executed by one or moreprocessors in a computer, cause the computer to perform operationsnecessary to execute elements involving the various aspects.

A machine readable medium can be used to store software and data whichwhen executed by a data processing system causes the system to performvarious methods. The executable software and data may be stored invarious places including for example ROM, volatile RAM, non-volatilememory and/or cache. Portions of this software and/or data may be storedin any one of these storage devices. Further, the data and instructionscan be obtained from centralized servers or peer to peer networks.Different portions of the data and instructions can be obtained fromdifferent centralized servers and/or peer to peer networks at differenttimes and in different communication sessions or in a same communicationsession. The data and instructions can be obtained in entirety prior tothe execution of the applications. Alternatively, portions of the dataand instructions can be obtained dynamically, just in time, when neededfor execution. Thus, it is not required that the data and instructionsbe on a machine readable medium in entirety at a particular instance oftime.

Examples of computer-readable media include but are not limited tonon-transitory, recordable and non-recordable type media such asvolatile and non-volatile memory devices, Read Only Memory (ROM), RandomAccess Memory (RAM), flash memory devices, floppy and other removabledisks, magnetic disk storage media, optical storage media (e.g., CompactDisk Read-Only Memory (CD ROM), Digital Versatile Disks (DVDs), etc.),among others. The computer-readable media may store the instructions.

The instructions may also be embodied in digital and analogcommunication links for electrical, optical, acoustical or other formsof propagated signals, such as carrier waves, infrared signals, digitalsignals, etc. However, propagated signals, such as carrier waves,infrared signals, digital signals, etc. are not tangible machinereadable medium and are not configured to store instructions.

In general, a machine readable medium includes any mechanism thatprovides (i.e., stores and/or transmits) information in a formaccessible by a machine (e.g., a computer, network device, personaldigital assistant, manufacturing tool, any device with a set of one ormore processors, etc.).

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement the techniques. Thus, thetechniques are neither limited to any specific combination of hardwarecircuitry and software nor to any particular source for the instructionsexecuted by the data processing system.

The above description and drawings are illustrative and are not to beconstrued as limiting. Numerous specific details are described toprovide a thorough understanding. However, in certain instances, wellknown or conventional details are not described in order to avoidobscuring the description. References to one or an embodiment in thepresent disclosure are not necessarily references to the sameembodiment; and, such references mean at least one.

In the foregoing specification, the disclosure has been described withreference to specific exemplary embodiments thereof. It will be evidentthat various modifications may be made thereto without departing fromthe broader spirit and scope as set forth in the following claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative sense rather than a restrictive sense.

What is claimed is:
 1. A method, comprising: receiving, in a devicehaving a first portion of an artificial neural network, data from asensor and an input generated outside of the device according to asecond portion of the artificial neural network, the artificial neuralnetwork configured to generate at least one output based on a pluralityof sensors, including the sensor connected to the device; executing, bythe device, instructions to implement matrix computations of the firstportion of the artificial neural network to generate a first outputindependent of the input generated outside of the device; andgenerating, by the device, a second output according to the firstportion of the artificial neural network based on a combination of thedata from the sensor and the input data generated outside of the deviceaccording to a second portion of the artificial neural network.
 2. Themethod of claim 1, further comprising: writing, into the random accessmemory of the device, instructions of an application; executing, by acentral process unit of the device, the instructions of the applicationto convert a description of the artificial neural network into: datarepresentative of parameters of portions of the artificial neuralnetwork, and data representative of instructions executable to implementmatrix computations of the portions of the artificial neural network;distributing computation tasks of the portions of the artificial neuralnetwork to a plurality of devices.
 3. The method of claim 2, wherein theartificial neural network is configured to generate an output from aplurality of imaging devices, including the sensor; a portion of theplurality of imaging devices is not configured in the device; a portionof the first output includes features recognized from the data from thesensor by the first portion of the artificial neural network; and themethod further comprises: communicating, via at least one interface ofthe device, with at least one external device to obtain the inputgenerated outside of the device according to the second portion of theartificial neural network; and communicating, via the at least oneinterface, with the at least one external device to provide the portionof the first output as an input to a third portion of the artificialneural network.
 4. The method of claim 3, wherein an output of theartificial neural network includes an identification or classificationof an object captured in image data generated by the plurality ofimaging devices.
 5. A device, comprising: random access memory; at leastone interface configured to receive data from a sensor as an input to atleast a first portion of an artificial neural network and to receive aninput generated outside of the device according to a second portion ofthe artificial neural network, the artificial neural network configuredto generate at least one output based on a plurality of sensors,including the sensor connected to the device; and at least oneprocessing unit coupled to the random access memory and the at least oneinterface to execute instructions having matrix operands to implementthe matrix computations of the first portion of the artificial neuralnetwork, the first portion of the artificial neural network configuredto process the data from the sensor to generate a first outputindependent of the input generated outside of the device according tothe second portion of the artificial neural network and to generate asecond output based on a combination of the data from the sensor and theinput generated outside of the device according to the second portion ofthe artificial neural network.
 6. The device of claim 5, furthercomprising: the sensor configured within the device, wherein the firstoutput includes an identification or classification of an objectdetermined independent of the input generated outside of the deviceaccording to the second portion of the artificial neural network.
 7. Thedevice of claim 6, wherein the sensor is a first sensor; and the inputgenerated according to the second portion of the artificial neuralnetwork is based on data from a second sensor configured outside of thedevice.
 8. The device of claim 7, wherein the first sensor and thesecond sensor are imaging sensors; and the second output includes anidentification or classification of the object determined based on boththe first sensor and the second sensor.
 9. The device of claim 5,wherein computation of a third portion of the artificial neural networkis configured outside of the device; and the at least one interface isconfigured to provide at least a portion of the first output to outsideof the device as an input to the third portion of the artificial neuralnetwork.
 10. The device of claim 9, wherein the portion of the firstoutput includes features in the data from the sensor recognized via thefirst portion of the artificial neural network.
 11. The device of claim9, wherein the device is further configured to communicate via the atleast one interface to identify an external device operable to implementmatrix computations of the third portion of the artificial neuralnetwork and write the portion of the first output into random accessmemory of the external device.
 12. The device of claim 5, wherein thedevice is further configured to communicate through the at least oneinterface to determine whether to perform computation to generate thesecond output; and wherein the first output is generated independent ofwhether the computation to generate the second output is performed. 13.The device of claim 5, further comprising: a central process unitconfigured to run an application stored in the random access memory,wherein the application is configured to generate, based on adescription of the artificial neural network, data representative ofparameters of portions of the artificial neural network, and datarepresentative of instructions executable to implement matrixcomputations of the portions of the artificial neural network; whereinthe application is further configured to distribute computations of theportions of the artificial neural network to a plurality of devices; andwherein the at least one interface includes a plurality of interfacesconfigured to be connected to the plurality of devices over a pluralityof serial connections respectively.
 14. The device of claim 5, furthercomprising: an integrated circuit die of a Field-Programmable Gate Array(FPGA) or Application Specific Integrated circuit (ASIC) implementing aDeep Learning Accelerator, the Deep Learning Accelerator comprising theat least one processing unit, and a control unit configured to load theinstructions from the random access memory for execution.
 15. The deviceof claim 14, wherein the at least one processing unit includes amatrix-matrix unit configured to operate on two matrix operands of aninstruction; wherein the matrix-matrix unit includes a plurality ofmatrix-vector units configured to operate in parallel; wherein each ofthe plurality of matrix-vector units includes a plurality ofvector-vector units configured to operate in parallel; and wherein eachof the plurality of vector-vector units includes a plurality ofmultiply-accumulate units configured to operate in parallel.
 16. Thedevice of claim 15, wherein the random access memory and the DeepLearning Accelerator are formed on separate integrated circuit dies andconnected by Through-Silicon Vias (TSVs); and the device furthercomprises: an integrated circuit package configured to enclose at leastthe random access memory and the Deep Learning Accelerator.
 17. Anapparatus, comprising: memory configured to store first datarepresentative of parameters of a first portion of an artificial neuralnetwork, second data representative of instructions executable toimplement matrix computations of the first portion the artificial neuralnetwork using at least the first data stored in the memory, third datafrom a sensor, and fourth data generated outside of the apparatusaccording to a second portion of the artificial neural network; at leastone interface configured to receive the fourth data from outside of theapparatus and store the fourth data into the memory; a connection to thememory; and a Field-Programmable Gate Array (FPGA) or ApplicationSpecific Integrated circuit (ASIC) having: a memory interface configureto access the memory via the connection; and at least one processingunit configured to execute the instructions having matrix operands toimplement the matrix computations of first portion of the artificialneural network, the first portion of the artificial neural networkconfigured to process the third data from the sensor to generate a firstoutput independent of the fourth data and to generate a second outputbased on a combination of the third data from the sensor and the fourthdata generated according to the second portion of the artificial neuralnetwork.
 18. The apparatus of claim 17, further comprising: a centralprocess unit configured to execute instructions of a compiler configuredto partition the artificial neural network into portions, generate froma description of the artificial neural network to a plurality ofdatasets representative of the portions of the artificial neuralnetwork, and distribute the plurality of datasets to a plurality ofintegrated circuit devices, each of the plurality of integrated circuitdevices operable to implement computations of a portion of theartificial neural network based on a corresponding dataset in theplurality of datasets.
 19. The apparatus of claim 17, furthercomprising: the sensor configured within the apparatus; wherein thefirst output includes a first identification or classification of anobject determined independent of the fourth data; and wherein the secondoutput includes a second identification or classification of the objectdetermined from the third data and the fourth data.
 20. The apparatus ofclaim 17, wherein the at least one interface includes a serialcommunications interface to an integrated circuit device configuredoutside of the apparatus and having an FPGA or ASIC operable toimplement matrix computations of the artificial neural network; and theapparatus includes a radar imaging device, a lidar imaging device, adigital camera, an infrared camera, or an ultrasound imaging device, orany combination thereof.